STAR-RIS-Assisted Covert Communications in RSMA Networks: A Quantum Reinforcement Learning Approach

Due to its capability to ensure communication security and improve spectral efficiency, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted covert communications in Rate-Splitting Multiple Access (RSMA) networks have drawn widespread attention. However, existing studies often overlook the impact of the mobility of wardens and users on long-term covert performance, and lack joint optimization of covert rate and energy consumption. Meanwhile, introducing STAR-RIS significantly increases system parameter dimensionality, rendering traditional methods inefficient. Although reinforcement learning offers advantages, it still faces challenges in training efficiency and resource overhead within high-dimensional state-action spaces. Therefore, this paper proposes a quantum reinforcement learning-based algorithm, named QUERC, for STAR-RIS-assisted covert communications in RSMA networks. Specifically, we first formulate a long-term covert energy efficiency maximization problem under a dynamic environment with mobile users and wardens. Then, we propose a novel hybrid quantum neural network architecture to solve this problem. This architecture integrates a fully connected layer, a variational quantum circuit, an action post-processing mechanism, and a regularized objective, enhancing policy stability and generalization in high-dimensional action spaces. Finally, extensive experiments demonstrate that, compared with TPG, QPG, RIS-NOMA, RPS, and greedy approaches, QUERC algorithm achieves superior average covert energy efficiency and offers significant advantages in computational and inference performance.